Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) was originally founded in 1959. The publisher of the journal is Wuhan University of Technology. JWUT first got the scopus license in the year 2001. The journal generally publishes all aspect of engineering sciences like: physics, chemistry, mathematics, and all sorts of general engineering.
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) (ISSN:2095-3844) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to: :
The mining of IC card data for commuters’ classification and analysis of bus route choice plays an important role for public transport passengers’ choice behavior analysis, and formulating a scientific and reasonable traffic planning strategy. The authors choose some indicators for passengers’ classification in Beijing and use classification and regression tree model to build the classifier. Three parameters: departure time, travel distance and travel frequency, as the classification parameters, are input into the model and the results show 45 classification passengers; Among them, strong commuting passengers accounted for 7.25%, weak commuting passengers accounted for 30.27%, and accidental commuting passengers accounted for 62.48. This paper also analyzes the characteristics of the public transport route choice for all classification groups from four aspects: the overall characteristics, transfer characteristics, travel characteristics and subway travel characteristics. The conclusions of this paper can provide a theoretical basis for the analysis of passenger flow composition of multimodel public transport and the establishment of multi-mode bus route selection model.
Roadside parking systems plays an important role in the planning and design of parking spaces, parking management and operation, and ensuring the safety of passengers. Firstly, we proposed a traffic wave theory and an avoidance logic algorithm to analyze the interaction mechanism of the roadside parking system in a rail-integrated transport hub. Moreover, we researched the evaluation indexes of the stability of roadside parking systems via two new concepts, namely static roadside parking time and dynamic roadside parking time. We found these improved the algorithm of the time utilization rate of the berth. Secondly, the coupling relationship between parking rate, delay and time utilization of berth was also discussed, and mathematical models of stability of the curb parking system were established. Furthermore, the feasibility of models was verified by multi-agent simulation through the VISSIM simulation platform. Finally, a new rail-integrated transport hub was taken as a practical case and studied alongside the simulation of the stability of curb parking system. Our study concluded that the curb parking system was ultimate stable when the time utilization rate was 37.5% and the parking rate was 91.2%. The results of these studies will provide theoretical support for designing curb parking berths in rail-integrated transport hubs.
The application of drones provides a powerful solution for “the last-mile” logistics services, while the large-scale implementation of logistics drone services will threaten the safety of buildings, pedestrians, vehicles, and other elements in the urban environment. The balance of risk cost and service benefit is accordingly crucial to managing logistics drones. In this study, we proposed a cost-benefit assessment model for quantifying risk cost and service benefit in the urban environment. In addition, a global heuristic path search rule was developed to solve the path planning problem based on risk mitigation and customer service. The cost-benefit assessment model quantifies the risk cost from three environmental elements (buildings, pedestrians, and vehicles) threatened by drone operations based on the collision probability, and the service benefit based on the characteristics of logistics service customers. To explore the effectiveness of the model in this paper, we simulate and analyse the effects of different risk combinations, unknown risk zones, and risk-benefit preferences on the path planning results. The results show that compared with the traditional shortest-distance method, the drone path planning method proposed in this paper can accurately capture the distribution of risks and customers in the urban environment. It is highly reusable in ensuring service benefits while reducing risk costs and generating a cost-effective path for logistics drones. We also compare the algorithm in this paper with the A* algorithm and verify that our algorithm improves the solution quality in complex environments.
In developing countries, heavy-duty trucks play an important role in transportation for infrastructure construction. However, frequent truck accidents cause great losses. Previous studies have mainly focused on passenger drivers; to date, little has been done to assess the driving behavior of heavy truck drivers. The overall objective of this study is to classify driving styles at intersections, analyze the impacts of differing types of traffic control at intersections on driving styles, and identify potentially risky intersections. We selected 11 heavy-duty truck drivers and collected kinematic driving parameters (including driving speed and both lateral and longitudinal acceleration) from field experiments in Nanjing for our study. Our study on driving styles followed the following steps. First, we reduced data size and extracted data features on the basis of time windows in Python. Second, driving styles were classified into three driving styles: cautious, normal, and aggressive, based on the K-means clustering method, and the corresponding thresholds for each category were obtained. Kinematic driving parameters were used as driving style measurements. Third, according to classifications of driving style, the impacts of four different intersection traffic control types: two-phase signalized, multiphase signalized, stop, and yield intersections, on driving styles have been analyzed using the multinomial logit model. Moreover, based on the above analysis, potentially risky intersections were identified. The results suggest that different types of traffic control at intersections lead to variations in driving styles and have different influences on driving styles. In terms of accuracy, our method, which uses driving speed, both lateral and longitudinal acceleration, and jerk as features, performs better than traditional methods which only use speed and acceleration. The results of the study allow us to analyze the driving data of heavy-duty trucks and identify drivers who drive more aggressively during a trip. In addition, the results show that aggressive driving styles mostly occur at stop intersections and in the dilemma zones of signalized intersections. Therefore, early-warning interventions can be provided during a driver’s trip by analyzing the different types of traffic control at intersections on the route in advance. Finally, the cumulative analysis of driving styles at intersections over multiple trips can be used to identify potentially high-risk intersections. It is possible to eliminate potential risks in these areas through measures such as early warnings and by improving traffic management control methods.
The paper presents the results of the experimental and numerical analysis of a six-hole orifice flow meter. The experiments were performed on humid air in a 100 mm diameter duct. The aim of this research was to investigate the mass flow and pressure drop dependency in an orifice of a predetermined shape and to compare the results obtained with computational formulas recommended in the ISO 5167-2 standard for a single-hole orifice flow meter. The experiments and calculations were performed on several multi-hole orifice geometries with different contraction coefficient in a wide range of Reynolds numbers. The pressure was probed immediately upstream and downstream of the orifice. The flow coefficient determined for the six-hole orifice flow meter investigated was compared with the flow coefficient of conventional single-hole orifice with the same contraction coefficient. The results from computational formulas for single-hole orifice from ISO 5167 are also included in the paper. During some experiments, an obstacle has been introduced in the duct at variable distance upstream from the orifice. The effect of the thus generated velocity field disturbance on the measured pressure drop was then investigated. Numerical simulation of the flow with the presence of the obstacle was also performed and compared with experimental data.
Many achievements toward unmanned surface vehicles have been made using artificial intelligence theory to assist the decisions of the navigator. In particular, there has been rapid development in autonomous collision avoidance techniques that employ the intelligent algorithm of deep reinforcement learning. A novel USV collision avoidance algorithm based on deep reinforcement learning theory for real-time maneuvering is proposed. Many improvements toward the autonomous learning framework are carried out to improve the performance of USV collision avoidance, including prioritized experience replay, noisy network, double learning, and dueling architecture, which can significantly enhance the training effect. Additionally, considering the characteristics of the USV collision avoidance problem, two effective methods to enhance training efficiency are proposed. For better training, considering the international regulations for preventing collisions at sea and USV maneuverability, a complete and reliable USV collision avoidance training system is established, demonstrating an efficient learning process in complex encounter situations. A reward signal system in line with the USV characteristics is designed. Based on the Unity maritime virtual simulation platform, an abundant simulation environment for training and testing is designed. Through detailed analysis, verification, and comparison, the improved algorithm outperforms the pre-improved algorithm in terms of stability, average reward, rules learning, and collision avoidance effect, reducing 26.60% more accumulated course deviation and saving 1.13% more time.
A wide range of research activities exploit spaceborne Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for applications that contribute to maritime safety and security. An important requirement of SAR and AIS data fusion is accurate data association (or correlation), which is the process of linking SAR ship detections and AIS observations considered to be of a common origin. The data association is particularly difficult in dense shipping environments, where ships detected in SAR imagery can be wrongly associated with AIS observations. This often results in an erroneous and/or inaccurate maritime picture. Therefore, a classification-aided data association technique is proposed which uses a transfer learning method to classify ship types in SAR imagery. Specifically, a ship classification model is first trained on AIS data and then transferred to make predictions on SAR ship detections. These predictions are subsequently used in the data association which uses a rank-ordered assignment technique to provide a robust match between the data. Two case studies in the UK are used to evaluate the performance of the classification-aided data association technique based on the types of SAR product used for maritime surveillance: wide-area and large-scale data association in the English Channel and focused data association in the Solent. Results show a high level of correspondence between the data that is robust to dense shipping or high traffic, and the confidence in the data association is improved when using class (i.e., ship type) information.
With the development of society, the demand for mineral resources is gradually increasing, and the current situation of decreasing total resources dictates the inevitable interaction between open-pit combing underground extraction (OPUG) in time and space. In this research, we took the Anjialing coal mine in Shanxi Province of China as a case study, and tested the physical and mechanical properties of coal rocks in the laboratory. The similarity criterion was used to build a similar experimental model for the deformation evolution of the slope of the open-pit mine section; the digital scattering method was used to test the influence of the underground mining process parameters on the deformation evolution of the open-pit slope. The results showed that there was an obvious distribution of “three zones” above the mining goaf, namely, a collapse zone, fracture zone, and slow subsidence zone. When the mining face was continuously advanced towards the bottom of the open pit, the supporting stress of the mining face transferred to the side of the open-pit slope. Additionally, large displacement and stress concentration were observed on the slope near the stoping line, which caused the slope body to move along the uppermost part of the slope first, and thereafter along the lower part. Various techniques for slope stability control are discussed, including the optimization of spatial and temporal relationships between open-pit and underground mining, the optimization of mining plans, and the use of monitoring and early warning systems. The results can provide a guide for slope stability control of similar open-pit mines in the process of mining coal resources.
The K-type joint, which consists of the web members and the chord members with varied angles welded together, has been widely adopted in long-span steel truss bridges. However, its fatigue performance has been rarely considered, despite its critical role in bridge structural safety and durability. Accordingly, the FE model of the K-type joint was established in Abaqus and the fatigue performance analysis was conducted, in which the effect of web/chord thickness ratio (τ), chord/web angle (θ), and chord with rib stiffener were investigated. Take the Mingzhu Bay steel truss arch bridge as an engineering background, the hot spot stress method was employed to calculate the fatigue performance of three K-type joints in unfavorable locations. Furthermore, a 3D full-scall bridge model was built to evaluate the fatigue performance of the K-type joints under standard and overloaded moving vehicle load scenarios. The results show that the max hot spot stress factor (SCFmax) of the web and chord member is influenced by τ and θ. The chord members added stiffener is founded to be an effective way to enhance fatigue performance. The fatigue stress intensities of the three unfavorable locations meet the Eurocode 3 specification requirements, but the one in the mid-truss arch is not satisfied under an overloaded vehicle loading rate of 25%.
Rockburst hazards induced by high geostress are particularly prominent during the construction of underground engineering. Prevention and control of rockburst is still a global challenge in the field of geotechnical engineering, which is of great significance. Based on the tunnel group of the Jinping II hydropower station of China, this paper analyzed the mechanical principle of support in the process of construction, and discussed in detail the active release and passive support by numerical simulation and field application. The results show that as two active measures, stress relieve holes and advanced stress relief blasting can release the energy of the microseismic source and transfer the high stress to the deeper surrounding rock, make the surface rock wall with a relatively low stress act as a protective barrier. Their stress release rate is about 12% and 33% in this project, respectively. In term of passive measure, the combined rapid support, which is mainly composed of water swelling anchor and nano-admixture shotcrete, is also an effective way to prevent and control the rockburst under high geostress.
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